Towards Architecting Sustainable MLOps: A Self-Adaptation Approach

Understand

In today's dynamic technological landscape, sustainability has emerged as a pivotal concern, especially with respect to architecting Machine Learning enabled Systems (MLS).

• Many ML models fail in transitioning to production, primarily hindered by uncertainties due to data variations, evolving requirements, and model instabilities.
• Machine Learning Operations (MLOps) offers a promising solution by enhancing adaptability and technical sustainability in MLS.
• However, MLOps itself faces challenges related to environmental impact, technical maintenance, and economic concerns.